In the field of medical images, it is common to process images of an anatomical feature in order to enhance a low contrast image for clinical study. In particular, medical image data is often of low contrast, such that differences in the image are hard to detect by the human eye. Thus, the difference between a pixel of the image with the highest intensity and a pixel of the image with the lowest intensity is often very small. To enhance the image contrast, a series of filters can be used.
In one method, the medical image is processed using a technique known as image pyramiding. A first pyramid, which is sometimes called a Gaussian pyramid, is constructed by repetitive applications of anti-aliasing filtering followed by downsampling, which results in a series of different Gaussian images. A second pyramid, which is sometimes called a Laplacian pyramid, is constructed by obtaining a difference between (a) an upsampled and interpolation-filtered version of one of the Gaussian images and (b) the Gaussian image of the corresponding size. An output higher-contrast image can then be constructed from every level of the Gaussian and Laplacian images.
Typically, the filters of the Gaussian pyramid are symmetrical to the filters of the Laplacian pyramid. More specifically, it is often the case that the size of the anti-aliasing filter is the same as the size of the interpolation filter, and in particular that both are 5×5 pixels. The symmetric 5×5 pixel filters allow for calculation of difference data at relatively low computational cost as compared to larger filters.